1. Field of the Invention
The present invention relates generally to natural language translation. More specifically, the present invention relates to providing machine-generated translations and corresponding trust levels.
2. Related Art
Machine translation involves use of computer systems to translate text or speech from one natural language to another. Using corpus techniques, more complex translations can be achieved relative to simple word substitution approaches. Parallel corpora or other training datasets may be used to train, or effectively ‘teach,’ a machine translation engine to translate between two languages, thus allowing for better handling of differences in linguistic typology, phrase recognition, translation of idioms, and isolation of anomalies.
Presently, machine-generated translations are provided without any quantified assurance of translational accuracy. Without any assurance, machine translation users may unknowingly risk sending and receiving misinformation to contacts, clients, customers, colleagues, and so forth. In order for a consumer to obtain such assurance of translation accuracy for a given machine-generated translation, the users must either possess some degree of familiarity with the source and target languages, rely on another individual with that familiarity, or obtain a human-generated translation for comparison with the machine-generated translation. In all of these cases, human expertise is necessitated. Counter to the objective of machine translation, limited supply of human expertise therefore still can hamper efficient and effective dissemination of information across language barriers.
In some machine translation systems, feedback associated with translational accuracy can be provided for improving those systems, but that feedback is not useful for machine translation users that need an indication of translational accuracy before sending or when receiving a translation. Such feedback is requested and provided subsequent to translations being provided and is often on a voluntary basis rendering availability of this feedback undependable. In addition, multiple individuals with varying levels of fluency in the pertinent languages may provide the feedback. As such, an accuracy metric or rating scale determined by multiple individuals is nearly impossible to standardize. Furthermore, feedback may not be available for some translated information due, for example, to sensitivity of that information. Therefore, there is a need for machine-generated translations to be provided concurrently with an indication of translational accuracy, without human involvement.